{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Введение в анализ данных\n", "## Домашнее задание 3. Оценки параметров и сбор данных.\n", "**Выполнил: Евдокимов Егор**\n", "\n", "Cсылка на [drive](https://drive.google.com/drive/folders/1UKfnCjGAkPKN8mxcIAfXCQFhhRcJWjVR?usp=sharing) со всем файлами." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Правила, прочитайте внимательно:**\n", "\n", "* Выполненную работу нужно отправить телеграм-боту `@miptstats_ad21_bot`. Для начала работы с ботом каждый раз отправляйте `/start`. Работы, присланные иным способом, не принимаются.\n", "* Дедлайн см. в боте. После дедлайна работы не принимаются кроме случаев наличия уважительной причины.\n", "* Прислать нужно ноутбук в формате ipynb и **в формате html**, который можно получить как `File -> Download as -> HTML`.\n", "* Теоретические задачи необходимо прислать одним из следующих способов:\n", " * фотографией в правильной ориентации, где все четко видно, а почерк разборчив;\n", " * фотографией, **встроенной в ноутбук** посредством `Edit -> Insert Image`;\n", " * в виде $LaTeX$ в markdown-ячейках.\n", "* Теоретические задачи не проверяются, если какое-то требование не выполнено. Особенно внимательно все проверьте в случае выбора второго пункта. Неправильно вставленные фотографии могут не передаться при отправке.\n", "* Будьте внимательны при работе со сбором данных. Ответственность за корректность ваших действий лежит на вас. Не нагружайте сервера, *делайте паузы между запросами*. Как следствие, начинайте выполнять задание заранее. **Если вас где-то забаннили и т.п., то это не является уважительной причиной продления дедлайна.**\n", "* Выполнять задание необходимо полностью самостоятельно. **При обнаружении списывания все участники списывания будут сдавать устный зачет.**\n", "* Решения, размещенные на каких-либо интернет-ресурсах, не принимаются. Кроме того, публикация решения в открытом доступе может быть приравнена к предоставлении возможности списать.\n", "* Для выполнения задания используйте этот ноутбук в качестве основы, ничего не удаляя из него. Можно добавлять необходимое количество ячеек.\n", "* Комментарии к решению пишите в markdown-ячейках.\n", "* Выполнение задания (ход решения, выводы и пр.) должно быть осуществлено на русском языке.\n", "* Если код будет не понятен проверяющему, оценка может быть снижена.\n", "* Никакой код из данного задания при проверке запускаться не будет. *Если код студента не выполнен, недописан и т.д., то он не оценивается.*\n", "\n", "\n", "**Баллы за задание:**\n", "\n", "Легкая часть (достаточно на \"хор\"):\n", "* Задача 1 — 3 балла\n", "* Задача 2 — 5 баллов\n", "* Задача 3 — 12 баллов\n", "\n", "Сложная часть (необходимо на \"отл\"):\n", "* Задача 4 — 6 баллов\n", "* Задача 5 — 10 баллов" ] }, { "cell_type": "code", "execution_count": 1293, "metadata": { "colab": {}, "colab_type": "code", "id": "ht9Xm0EucoAr" }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "sns.set(style='whitegrid', font_scale=1.3, palette='Set2')\n", "%matplotlib inline\n", "\n", "import requests\n", "from time import sleep\n", "import json\n", "# добавленные\n", "import scipy.stats as sps\n", "from collections import Counter\n", "from collections import OrderedDict\n", "# plotly\n", "import plotly\n", "import plotly.graph_objects as go\n", "import plotly.express as px\n", "import plotly.io as pio\n", "import plotly.figure_factory as ff" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Легкая часть" ] }, { "cell_type": "markdown", "metadata": { "id": "hYgTespbkXej" }, "source": [ "### Задача 1. \n", "\n", "**a).** Пусть $X_1,...,X_n$ выборка из некоторого распределения $\\mathsf{P}$, причем ${\\sf D} X_1=\\sigma^2<+\\infty$, и $\\sigma$ неизвестно. \n", "Рассмотрим оценку $S^2 = \\overline{X^2}-\\overline{X}^2$ дисперсии $\\sigma^2$.\n", "\n", "Докажите, что $S^2=\\frac1n\\sum\\limits_{i=1}^n\\left(X_i-\\overline{X}\\right)^2$.\n", "\n", "**Доказательство:**\n", "Согласно определению $S^2 = \\frac{1}{n} \\sum\\limits_{i=1}^n X_i^2 - (\\frac{1}{n} \\sum\\limits_{i=1}^n X_i)^2 = \n", "\\frac{1}{n} (\\sum\\limits_{i=1}^{n}X_i^2 - 2(\\sum\\limits_{i=1}^{n}X_i)(\\frac{1}{n} \\sum\\limits_{i=1}^{n}X_i) + n(\\frac{1}{n}\\sum\\limits_{i=1}^n X_i)^{2}) = \\frac{1}{n}((\\sum\\limits_{i=1}^n X_i^2) - 2\\overline{X}(\\sum\\limits_{i=1}^n X_i) + n(\\overline{X})^{2}) = \\frac{1}{n}(\\sum\\limits_{i=1}^n X_i^2 -2 \\overline{X}\\sum\\limits_{i=1}^n X_{i} + \\sum\\limits_{i=1}^n \\overline{X}^{2})$\n", "Получаем, что $S^2=\\frac1n\\sum\\limits_{i=1}^n\\left(X_i-\\overline{X}\\right)^2$\n", "\n", "**b).** Оценка $\\widehat{\\theta}$ называется несмещенной оценкой параметра $\\theta$ если для любого $\\theta \\in \\Theta$ выполнено $\\mathsf{E}_\\theta \\widehat{\\theta} = \\theta$. Иначе говоря, какое бы ни оказалось истинное значение параметра $\\theta$ рассматривая оценку $\\widehat{\\theta}$ в среднем будем получать именно $\\theta$.\n", "\n", "Является ли статистика $S^2$ несмещенной оценкой $\\sigma^2$?\n", "\n", "*Подсказка.* Посчитайте математическое ожидание случайной величины $S^2$. Используйте для этого известные вам свойства математического ожидания.\n", "\n", "**Доказательство:**\n", "Так как $X_1, ... X_n$ одинаково распределены, то $\\forall i \\mathsf E X_i = \\mathsf E X_1$. А так как они независимы $\\mathsf E(X_i X_j) = \\mathsf E X_i \\cdot \\mathsf E X_j$. Пользуясь линейностью матожидания\n", "\n", "$\\mathsf ES^{2} = \\frac{1}{n}\\sum\\limits_{i=1}^n \\mathsf EX_i^2 - \\mathsf E(\\frac{1}{n}\\sum\\limits_{i=1}^n X_{i})^{2} = \\mathsf E X^{2}_{1} - \\frac{1}{n^2}(\\sum\\limits_{i=1}^n \\mathsf EX_i^2 + 2\\sum\\limits_{1 \\leq i < j \\leq n}^n \\mathsf E(X_{i}X_{j}))=\n", " \\mathsf E X^{2}_{1} - \\frac{1}{n^2}(\\sum\\limits_{i=1}^n \\mathsf EX_i^2 + 2\\sum\\limits_{1 \\leq i < j \\leq n}^n \\mathsf E(X_{i}) \\mathsf E (X_{j}))\n", "= \\mathsf E X^{2}_{1} - \\frac{1}{n^{2}}(n \\mathsf EX_1^2 + 2\\frac{n(n-1)}{2} \\mathsf (E X_1)^2)\n", "= (\\frac{n - 1}{n})(\\mathsf EX_1^2 - (\\mathsf EX_1)^{2}) = (\\frac{n - 1}{n})\\sf DX_1.$\n", "В итоге статистика $S^2$ не является несмещенной оценкой $\\sigma^2$." ] }, { "cell_type": "markdown", "metadata": { "id": "ZH_7vCIE4Md7" }, "source": [ " ---\n", "### Задача 2. \n", "Пусть $X_1, ..., X_n$ — выборка из распределения $\\mathcal{N}(0, \\sigma^2)$. В качестве оценок $\\sigma^2$ обычно используют $S^2$ или $\\frac{n}{n-1}S^2$. Что можно сказать про их несмещенность?" ] }, { "cell_type": "markdown", "metadata": { "id": "jHlO95TVJGqx" }, "source": [ "**Ответ:** Оценка $S^2$, то есть просто выборочная дисперсия, является смещенной оценкой для теоретической. А исправленная, то есть $\\frac{n}{n-1} S^2$ является несмещенной. Так как теоритическая дисперсия для нормального распределения это как раз $\\sigma^2$ и по предыдущему пункту $\\mathsf E\\frac{n}{n-1}S^{2} = {\\sf D} X_1 = \\sigma^2$, а это соответствует определению несмещенной оценки. Тогда $S^2$ является смещенной оценкой, так как $\\mathsf E S^{2} \\neq \\sigma^2$." ] }, { "cell_type": "markdown", "metadata": { "id": "JP3jb2n64hgO" }, "source": [ "**1.** Теперь проверьте это на практике. Для каждой из приведенных выше оценок $\\widehat{\\theta}$ выполните следующие действия.\n", "\n", "* Вычислите $k = 500$ независимых оценок $\\widehat{\\theta}_1, ... , \\widehat{\\theta}_k$ по независимым выборкам $(X_1^1, ... , X_n^1), ... , (X_1^k, ... , X_n^k)$, сгенерированным из распределения $\\mathcal{N}(0, \\sigma^2)$. Далее вычислите среднее этих оценок, которое обозначим $\\overline{\\theta}$.\n", "\n", "* Визуализируйте полученные значения, построив на **одном** графике точки $(\\widehat{\\theta}_1, $y$), ... , (\\widehat{\\theta}_k, y)$ и среднее оценок $(\\overline{\\theta}, y)$, где $y$ — произвольные различные (например 0, 1, 2) координаты для двух различных типов оценок.\n", "\n", "* Повторите действие три раза для $n \\in \\{10, 100, 500\\}$. В итоге получится три графика для различных $n$, на каждом из которых изображено поведение двух типов оценок и их среднее.\n", "\n", "Используйте приведенный ниже **шаблон** для визуализации значений.\n", "\n", "**Внимание!** Следите за информативностью и наглядностью графиков. Например, пустых пространств должно быть как можно меньше, ничего не должно быть скомкано, вся нужная информация должна быть представлена. Посмотрите также презентацию с разбора задания 1. **Если график чему-то не удовлетворяет, оценка может быть снижена.**\n", "\n", "Вместо использования `matplotlib` можно также строить графики с помощью `plotly`." ] }, { "cell_type": "markdown", "metadata": { "id": "FdCCsVL43jva" }, "source": [ "**Решение:**" ] }, { "cell_type": "code", "execution_count": 1295, "metadata": { "id": "8qcnAu0a3jva" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "k = 500\n", "sigma = 1\n", "n_list = [10, 100, 500]\n", "\n", "# Вначале:\n", "plt.figure(figsize=(10, 12))\n", "\n", "for j, n in enumerate(n_list):\n", " teta = np.array([])\n", " teta_disp = np.array([])\n", " for i in range(k):\n", " sample = np.array(sps.norm(loc=0, scale=sigma).rvs(n))\n", "# sample minus average\n", " sample_min_av = sample - sample.mean()\n", "# sample minus average in square\n", " sample_min_av_sq = sample_min_av * sample_min_av\n", "\n", " teta_n = 1 / (n - 1) * sample_min_av_sq.sum()\n", " teta_n_disp = 1 / n * sample_min_av_sq.sum()\n", " teta = np.append(teta, teta_n)\n", " teta_disp = np.append(teta_disp, teta_n_disp)\n", " \n", " #отрисовка\n", " y = [0, 1]\n", " plt.subplot(3, 1, j + 1)\n", " # Для каждой оценки:\n", " plt.scatter(teta, np.zeros(k) + y[0], \n", " alpha=0.1, s=100, color='green', label='n/(n-1)*S^2')\n", " plt.scatter(teta.mean(), y[0], marker='*', s=200, \n", " color='w', edgecolors='black')\n", " \n", " plt.scatter(teta_disp, np.zeros(k) + y[1], \n", " alpha=0.1, s=100, color='orange', label='S^2')\n", " plt.scatter(teta_disp.mean(), y[1], marker='*', s=200, \n", " color='w', edgecolors='black')\n", " \n", " plt.title(f'Размер выборки = {n}')\n", " plt.vlines(1, 0, 1, color='r')\n", " plt.yticks([])\n", " plt.legend()\n", "\n", "plt.show()\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Вывод:** На всех трех графиках с разными размерами выборок видно, что несмещенная оценка всегда ближе к истинному значению $\\sigma$, чем смещенная. Зеленая звездочка всегда получается ближе к 1.\n", "Также заметно, что при увеличении $n$ разброс $\\theta$ уменьшается, да и $\\overline{\\theta}$ все точнее." ] }, { "cell_type": "markdown", "metadata": { "id": "-ZfBwUhe3jvb" }, "source": [ "**2.** Изучим поведение среднего оценок из первого пункта при росте размера выборки. Постройте график зависимости $\\overline{\\theta}$ от $n$ для двух типов оценок. Для вычисления зависимости нужно один раз сгенерировать выборки из пункта 1 достаточно большого размера (не более 500) и посчитать оценки по подвыборкам, используя функции из `numpy`. Использовать циклы, а так же функции, разворачивающиеся в цикл (например, `np.vectorize`), запрещено. Какие из оценок являются асимптотически несмещёнными, т.е. $\\forall \\theta \\in \\Theta\\colon \\mathsf{E}_\\theta \\widehat{\\theta} \\to \\theta$ при $n\\to +\\infty$?" ] }, { "cell_type": "markdown", "metadata": { "id": "jIBkuOKb3jvb" }, "source": [ "**Решение:**" ] }, { "cell_type": "code", "execution_count": 1296, "metadata": {}, "outputs": [ { "data": { "image/png": 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O67u7Lxvv2JjefXvTs3fzDR4AAIA9XE/PjAROQ0466aScdNJJu3Wf//Iv/7Jb98fETC4+R6398doc+OEDc8ylx+TADx+Ytdc0fxtLAAAAgKkQPM1BfXf3ZfWXVmfzts3ZtGVTNm/bnNVXrE7f3c3exhIAAABgKgRPc9DGOzama+HI21h2LuzMxjs2tqdCAAAAAGMQPM1Bvfv2Zuv2kbex7N/en959e9tTIQAAAIAxCJ7moJ69e7Lm+DXpXtSdZYuXpXtRd9Ycv8YE4wAAAIxpYGCg3VVgDtuVz4+72s1Rq5avysqDVrqrHQAAABPq7OzM5s2bs9dee7W7KsxRmzdvTmdn57S2FTzNYT179wicAAAAmNB+++2Xm266KQcccEC6u7vT0dHR7ioxRwwMDGTz5s256aabsv/++09rH4InAAAAmMeWLVuWJLn55pvT39/f5tow3NatW9PV1TX5im3U2dmZ/ffff+fnaKoETwAAADDPLVu2bNrBAc3ZsGFDnvjEJ7a7Go0yuTgAAAAAjRA8AQAAANAIwRMAAAAAjRA8AQAAANAIwRMAAAAAjRA8AQAAANAIwRMAAAAAjRA8zUN9d/dl/U3r03d335iPAQAAAGbConZXgF3Xd3dfNt6xMb379mbd/1uX1V9ana6FXdm6fWtWH7E6a76/ZufjNcevyarlq3bpGD1790y63tKupblr6107l5NtBwAAAMw/gqc5bu2P1+4MmrZs25Id2ZGt27dm87bNSZKPf/fjSbLz8eorVmflQSunFAINP8Z44VXf3X351Pc+lXO/eW4ykGzevjmdHZ3pH+hP96LuJJl26AUAAADMTYKnOazv7r6s/tLqbN62eWewNJnOhZ3ZeMfGloOnsY6x+orVOWz/w3b2ZFr3/9blDVe8Ifduv3fEtv0D/UnuC71OuPyEHLb/YTm059BWTxEAAACYwwRPc9jGOzama2FXy6FTkvRv70/vvr27dIyBgYEc/qnDs2TRktzbf292ZEf6d/RPuq8t27fk8E8dnotfdPGs7vk02XDByYYdGpY4s8Z6HX9yx0+yV99ec/51He8zMluWY72mrX7+AQCAPYPgaQ7r3bc3W7dvHVHW2dGZhQsW3q/3UZJ0L+rOmuPXTOlicKxjDO17y/YtU67zlu1bRvR8Gn1hPRMXq+Mdc7LhggMDA3lheWH+8fp/3Dm08U+O+pO89NCX7rwY/+JPv5hzv3nuiOdPXnHyiMBq+HrjHWf0fkdf7CeZtxf3rQZ/E72OC7Mw27+5fUqv63RClqmcx/D3rZVjjndus2U5NIT2gmMvyBEPPWLcz/90X+92L6f6fs1UsAcAAHNNx8DAwIwftJRyVJLTkqxI8rAkq6qq+ttJtulK8sEkr07ygCTfSvKWqqqua+WYGzZs6E3ys+XLl2fx4sW7UPv22LBhQ1asWHG/8rXXrM3qK1anc2Fn+rf3Z83xa/LgJQ/OS/7uJbm7/+6d6+3duXcue8Vlee5jnjvlYw8dY2BgYMxAazxDF6hj6VrQleMPOT7/eP0/7rywHiscGCtsmCyMmOiicfTF/PBA6cvVl6d0fq2e65KFS3YGVkPHnep+h1/sb9u+LR0LOtK9qHvKF/ejL2Sn26Nmst5e0z3e92/5fk796qmTBn9TfR0ne11bCVmmElyN9Tkbet8WZmFLQdJcsnjh4gmD6HaHZNN5v6f6fjVRh10NS1sN1db/aH2e/IQnz+pwrR1/pJgthvciTNobhs7noLeV5XTaylw5tz39nGbjuU30HbeV72DtPreZ/N0ym9435+bcpnNu63+0Ps9/6vPn9HebLVu25JprrkmSg1asWLFx9PPtCp6en+TpSTYkuSytBU8fS/KKJK9PclOS96UOrg6tququyY45X4On5P5DW/ru7suBHz5wxPC47kXd2XDShjF/oU1kaN9bt2/N0Z85etJeTl0LunLmM8/ceaHU6nZjGS9smCyMmOiicT6b6oXsdHvUjHdBPF6o1+rxlixcMu3gb6bNttDoIXcnvXckv+1KHrC19eXGfZPb957+/sbafmjb8fbN/bXymjUZqi3MwmzP9t12jN0drk32s2U+f4kd3ovwnq33tC0M3eeu/jxm08L8pnN7Hth//+UN+2zPpqVzN+htdTnVtjLbzm3ofRx6v6bzvo53TpPtay4se7Yvzn8t256OBR151G8WpG/Rlt16jKm0k4m+47byHWy8z2LP5jRybmMt9926IHd07Zjz79t8/kw6t9lzbg/e1plfPGhhPvSqi2b1lDQTmZXB03CllIFMEjyVUpYl6UtyUlVVnx4se0CSXyX5w6qq/mqy48zn4Gkso3tCrT5iddZ8f82IX1zJxHeaG343u3u33ZsFHQsmnE9q8cLF+cHJP7jf5OFrr1mbEy4/YVrhE7PfnhDqzZTpBEgv/WnyJ1cnAwPJXtuTLR3J4oHJl/csSjoGkg8clVz2uKnv755FSUeSPzw2+eHDRm67dWHSte3++55Ly4371u/JdAK96bx/Tbxmo4Os4QHXeOc2WWA43ZCzleNMFOSOPu7w7WfrRf7uCAlGn/NMfCan+jNmvJ8ls7G9zeefJVP9WfOlkhx3/fTe19HnNN3fQ7Ntec+iZNH2JAuSbdm957I72slYy6GL1lbWP/zm5MNfnfvv00y+b+1eOre5uZzOuQ19r37jizvz5xffNCd7Ps2X4Ok5Sa5K8jtVVd0yrPzrSW6qqur1kx1nTwiexur5NPRX3xUXrhgzNFqycEmueOUVOfxhh99vSNToXlOjLexYmM6Fnela2LVzmN94Ida1fdfm8E8dPufCp5kKVYQ3UzfWBfB4f2Wb6MJ3V3r3TKV+k118TPbFfWtH0jXGMql/UU3X0G+A6e5vIMnmhUn39vtvO7Tvdn8BmM1fhpp6zUZf5Ay/6OzeOva5TXZhtLsuLqd6ATb6uFO5IJ4NwcPuCHva+QW91Z8J0/3suvhoz/s4MM33daxzavUzQvt+L96zsH6vvE8wu93TmVTf+2oOf8Jz212VKZsvwdOrknw2SWdVVduHlV+aZL+qqp432XGGgqddr/HsdOVNV+b9//n+dC7oTP+O/pz1xLPyvAPql+Und/wkb/6PN+eubWOPSOxe2J0dAzty1hPPypEPOTI3b745N999c973o/eNmEenq6Mr6Ug6F3Rm245tI9b/ne7fyQMXP3DCOn71pq/mvT98b7YObN19J96Aro6urH7s6jznYc/JPdvvyV4L98pVt1yVi2+4OIsWLEr/9v4ctf9Rufq2qzMwMJCtA1t3dr9fvGBxBgYGctT+R+XffvVv457r0HrjHWf0fofvf9uObelIR7Zl2wy/Ms0Z3RNjqLv+UIA03kVZBurAY/vCjizcPpAdCxdmwfbt91sOPT8UrmxeVG/75ZK8cNhffMcLd3b1QnnzomTh4Bf17anrvLsDJBjL8N/wrX6+xrswmso+duU4rR63lQvidgcPTYc9AMD8sWlxUv3lx7PwsKe2uyq7QvCUedrjabw5nW58243jzvk0ls6OzixauGjciZuHzxM13Yld++7uy6c2fCrnXn1uOhd2Zuu2rffNAXTtF3Pu1edOOon5ZL2Dhg+3WJAFuXf7vePOKTJ0zOF1GboL3Vh1H69H2VhzZ010rhO9hq1ODjm076HXa7KhG5NpdQjIRCYaDjN6vaFw6RU/7ci7rx5I/8JkSX+yaOHCZMHCLNiyNQOLFqZj27DgaEHStaPex+64KGv1L77D10/GD6iESAAAwHRsW9KVRT//ZdIz/4baLZrxGk3Pramv4/ZLcsuw8v2T3NyWGs0iG+/YmK6FXSOCpc6Fndl4x8b07N2Tnr17sub4NSPuTDfWXaj6B/rTv23sYKF7UXfWHL/mfvM3TVXP3j0546gzcvKKk0eEOElyRs995Uu7lo4ZCo2YSHGC5yeaYHa8Y04Wpg29luM9nsq5Jkn6+pKfrk+WLk3uumvnsmfp0vTclWRpkrGWXfXmZ3Q/L2/8g5fmZ513ZZ87t2bb/7shi/d9cLbc8ev7LRc96jH5u75vjAiquhd158F37cjZva/Ps5b/jzG3G2v5r9f8Yz527SVZeu9Abl+0dedwtldetzDv+j/bs3DBwnRu3Z5tixelf/u2fPCZC/N3h9QT7d3ZNZAP3/WMPPNz38rAwEAWbNmeZKAOaYY6cO3YnrpfUNKxrV4u2l6nOYt3TPz5mqqphkND6w/1SBhv2VadnUl//+TLXd3fkiXJvXNjMnhg1w39eBv6g8DQMhG0zyWj/+CyO9/X8fY1V5Y7lizOgu07ko5kR0fHiD+A7epyOq9nEwaSDCzuTMeW/uZfz8Fe53P5fWv30rnNzeV0zm374q4sXLAwi9asmZOhUyvmSo+nocnFT6yq6jODZUtTTy7+tj19cvHJejwNX28oiPnFpl/kRZ9/0aS9oJJk7869c9krLstzHzPzY01H9zKa6vO7pxJ9ycaNSW9vaz8IhtYfCpSGthte/sUvJueeW6+/eXPrYUF3d7JtW9LRUf9/y5bkhS9M/vEfx99Xd3c95utP/iS/fv6z86tb64Bq8eX/lAM+fkk6OjqmVofOzgz03/8LSzLxcJih9WbDF685YSqficH3Ny996YgQc8Ll0GdwYKAOkKazv+9/Pzn11Pv2MbTt0Gdy9L7nynJ4O1uwoNlzaOo1m+yzNezchi4OGvuMTjf0ZOfPz46Z/ExO9WfMeD9LZmN728XlzrYyF89t9M+azs5k69apv69jndN0fw/NxmVvb93ohn+P2x3LabST4d+1Oib5DjbWevd7/5PkgguSI46Ykdfz2vXrc+iTnzy337fZsHRuc3M5xXO7dv36HPr858/p0GlWzvE0GBo9ZvDhD5K8O8lXk9xVVdUNpZQXJzkvydFVVd00uM3HkrwsyQmpezm9N8mTkhxaVdVdkx1zPgdPyf3vYjfRRN+jt5lsaNtYIda8NlZA1NVVhzyTfakaHSiN/qI3VN5O8/Hib6oXBy3uZ7e9Vq1cfOzKF/dWQ9HRRoek09lfK0HrbPgCMJu/DDXxmo2+yBn++Tr55BHntvPiYLILo911cTnVC7DRx53KBfEsCSx2S9jTzi/ok/1M2NXP7hy5+JjWhfRsOrfRP2um+76OdU7T/T20J2n378UZfo+mekdu2JPNh/YyW4OnZyX5xhhP/Z+qqp5VSnl9kouTHFRV1cbBbbqSfCjJq5MsTfLtJG+uquq6Vo4534OnZHq9fya621z3ovqvI62EWPNCX1/yqU+NDI7G4y/5zRjVE2O6f4Gf9OJg6MJ1sr/47q4L5VYvPnxxZ3caLxQcZcTvl8kujHbXZ3SqF2CjjzuVC+K5uvTzYNaZDxcGMFO0F2jdfGgvs3KOp6qq/jUTDHWuquqSJJeMKtua5NTBf4xhsjmHxnLX1ruyZNGSEcHT3p1756LjLspBDzyo2SFs7TLWBcsXv5icc07rc9YMhUyjl0zdBD0xpntRds899ySHTjAf2Rln1MdqdQjl8PV39aLRhSQzpadn6p+36WwzHbt6nMm2184AAGaNtgRPzB69+/Zm6/atI8p2DOzIsw969vwJnFqZW6ndpjpfRDJ+vafbC2t3Dw2ZyhxCo0Oa2XjhO1MX5AAAAPOI4GkPN/yOd8Pnh5qzodPweQuSyYfOtSt02tX5fJL7zm1XJgjd3ZOBDg+QJuolZAgJAADAHkHwRFYtX5WVB61s/u5wTRo+P1NXV3LPPXX5rgZLU73j1Hi9fsYKh3Y1fGl1uFg7AyC9hAAAAPZogieSTG9+qBk10US0o+dn2tW7xi1ZMnZA1OpkuOP1+mki5Gk12BEAAQAA0AaCJ5rR4t2U7rf+WLf6XbcuWb26Xm/4vEy7Y36msXoqnXzy+JNCTye8EfoAAACwhxI8sfutXTsyKJoo1Bk9RG7LlvuGtXV11b2YduwYGTDtyp3kJprXyLxDAAAAsFsJnti9rr02OeGEOkAaMjT07cwz6yFxw+c/GmuI3Be+MPLxdHR2Jh0d9bC53T23EgAAANASwRO7z9q19w+dRrv33jqAOvPMZuowND/TySfXj5uaWwkAAACYlOCJ3WOsnk5NmehOcqOH8gmcAAAAoG0ET+y6Vno67Q6LFydXXVXP/TTRneQAAACAWUHwxK7p66snEh8rdBoKir7xjZFzOY1lyZL7JhXv7KznZRr+uL8/WbMmefrTmzsXAAAAYLcSPDE9fX31/Ek/+1myYMH9n1+8OLn44jooevrT6yFwQ3evGxioQ6ixhsgN7XeoB9PoxwAAAMCcIXhi6taurXs5JWPfeW7x4uQHP0gOPfS+sp6e5Iwz6oBp48bxh8j19Ez8GAAAAJgzBE+0Zqjn0dKldeg0VuCU1L2Y1qwZGToNJ0gCAACAPYbgickN9XDq6qoDp46Osdfbe+/kssuS5z53ZusHAAAAzEqCJyY2NHn45s3j93IasmNHcvjhM1MvAAAAYNYbY1ZoGGbjxrqn00S6u+8bYmcYHQAAADBIjycm1tubbN069nN7751cdFFy0EHuOgcAAADcjx5PTKynp+7JtGTJ/Z/bsSN59rOTJz9Z6AQAAADcj+CJya1alfz858n7318PqVu2zNA6AAAAYFKG2jGxvr56nqfe3uSMM5KTT77vsdAJAAAAmIAeT4xv7drkwAOTY46pl2vX1mGToXUAAABACwRPjK2vL1m9Otm8Odm0qV6uXl2XAwAAALRA8MTYNm5MurpGlnV21uUAAAAALRA8Mbbe3mTr1pFl/f11OQAAAEALBE+M7/TT3cUOAAAAmDZ3teP+1q6t53Pq6koGBpLTTqvvZid0AgAAAKZAjydGuvba5IQT7ptU/N57k3PPbXetAAAAgDlI8MR91q5NDj882bJlZLlJxQEAAIBpEDxR6+urh9eNDp0Sk4oDAAAA0yJ4orZxYz2n02iLF5tUHAAAAJgWk4tT6+1Ntm4dWbZ4cfKDHySHHtqWKgEAAABzmx5P3Of005Pu7mTZsnp58cVCJwAAAGDa9Hja0/X1JZ/6VH3nuq6uZGAgOe205OSTDa8DAAAAdokeT3uytWuTRz4yOfPMZPPmZNOm5N576xAKAAAAYBcJnvZEfX3J175W38Xu3nvv/3xnZz3ZOAAAAMAuMNRuT7N2bR04LVhQ93IaS39/Pdk4AAAAwC4QPO1J+vrq0Gm8wCmpJxVfs8b8TgAAAMAuM9RuT7JxYz2B+Gh7750sWZK8//3JjTcmq1bNeNUAAACA+UePpz1Jb2+ydevIsiVLkssuSw4/XC8nAAAAYLfS42lP0NeXrF+f3H57cvrp9XC6Zcvq5UUXJc99rtAJAAAA2O30eJrvhiYTT+q5nbq7k4GB5LTTkpNPFjgBAAAAjdHjaT4bPpn40ITimzcn996bnHtue+sGAAAAzHuCp/lsvMnEk6Szs34eAAAAoCGCp/lsrMnEh/T3188DAAAANETwNJ/19CRr1tTzOi1ZUpd1d9f/1qwxvxMAAADQKJOLz3erViUrV9bD6pYuTe66q+7pJHQCAAAAGiZ42hP09AiaAAAAgBlnqB0AAAAAjRA8AQAAANAIwdN81teXrF9fLwEAAABmmOBpvlq7NjnwwOSYY+rl2rXtrhEAAACwhxE8zUd9fcnq1cnmzcmmTfVy9Wo9nwAAAIAZJXiajzZuTLq6RpZ1dtblAAAAADNE8DQf9fYmW7eOLOvvr8sBAAAAZojgaT7q6UnWrEm6u5Nly+rlmjV1OQAAAMAMWdTuCtCQVauSlSvr4XW9vUInAAAAYMYJnuaznh6BEwAAANA2htoBAAAA0AjB03zU15esX18vAQAAANpE8DTfrF2bHHhgcswx9XLt2nbXCAAAANhDCZ7mk76+ZPXqZPPmZNOmerl6tZ5PAAAAQFsInuaTjRuTrq6RZZ2ddTkAAADADBM8zSe9vcnWrSPL+vvrcgAAAIAZJniaT3p6kjVrku7uZNmyerlmTV0OAAAAMMMWtbsC7GarViUrV9bD63p7hU4AAABA2wie5qOeHoETAAAA0HaG2gEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQiEXtOGgp5bgk5yQ5OMnPk5xXVdVFk2zTm+T8JM9MsneSKskHq6r6u2ZrCwAAAMB0zHiPp1LKU5JcluSLSZ6Y5CNJLiylvGiSTa9I0pPk+Uken+Qfkvzt4P4AAAAAmGXa0ePp1CRXV1V19uDj6wbDo9OSXD7WBqWUpUmekOS4qqo2DBa/v5TytiRPSvKdJisMAAAAwNS1Y46npyX52qiyK5M8uZTSOdYGVVXdleQ/k/xBKWWfUsqCUsork3QnuarR2s4lfX3J+vX1EgAAAKDN2tHj6aFJfjWq7NYknUkekuSWcbZ7XpIvJLkjybYk9yR5SVVV103l4Ndcc81UVp9VNmzYMO5zD7zyyvS+//3Z0dmZBf392XjWWfnN8543g7WD2WWi9gKMpL1Aa7QVaJ32Aq2b7+2lLZOLT1UppSPJX6YOnJ6VZFOSlyT5fCnlmVVV/bDVfS1fvjyLFy9uopqN2rBhQ1asWDH2k319yTnnJFu2ZMGWLUmSR33gA8mJJyY9PTNYS5gdJmwvwAjaC7RGW4HWaS/QuvnQXrZs2TJhJ592BE+3Jtl/VNn+qUOl28fZ5tlJXppkv6qqhsaR/bCU8vQkb0vy+t1fzTlk48akqyvZvPm+ss7OulzwBAAAALRJO+Z4+naSY0aVHZvku1VV9Y+zzd6Dy+2jyrcn6diNdZubenuTrVtHlvX31+UAAAAAbdKOHk8XJPlWKeWsJJ9PcnSSVyV52dAKpZQXJzkvydFVVd2UOqzqS3JpKeXMJHemHmq3cnC5Z+vpSdasSVavrns69ffXj/V2AgAAANpoxns8VVX1ndTD5l6R5MdJ3pHk5KqqLh+22j5JSuoJx1NV1a+TPHfwua8m+WHqsOqEUdvtuVatSm68MVm3rl6uWtXuGgEAAAB7uLZMLl5V1RVJrpjg+UuSXDKq7IdJXtBkvea8nh69nAAAAIBZox1zPAEAAACwBxA8AQAAANAIwRMAAAAAjRA8AQAAANAIwRMAAAAAjRA8AQAAANAIwRMAAAAAjRA8AQAAANAIwdN80NeXrF9fLwEAAABmCcHTXLd2bXLggckxx9TLtWvbXSMAAACAJIKnua2vL1m9Otm8Odm0qV6uXq3nEwAAADArCJ7mso0bk66ukWWdnXU5AAAAQJsJnuay3t5k69aRZf39dTkAAABAmwme5rKenmTNmqS7O1m2rF6uWVOXAwAAALTZonZXgF20alWycmU9vK63V+gEAAAAzBqCp/mgp0fgBAAAAMw6htoBAAAA0AjBEwAAAACNEDwBAAAA0AjBEwAAAACNEDwBAAAA0AjBEwAAAACNEDwBAAAA0AjBEwAAAACNEDwBAAAA0AjBEwAAAACNEDwBAAAA0AjBEwAAAACNEDwBAAAA0AjBEwAAAACNEDwBAAAA0AjBEwAAAACNEDwBAAAA0AjBEwAAAACNEDzNZX19yfr19RIAAABglhE8zVVr1yYHHpgcc0y9XLu23TUCAAAAGEHwNBf19SWrVyebNyebNtXL1av1fAIAAABmFcHTXLRxY9LVNbKss7MuBwAAAJglBE9zUW9vsnXryLL+/rocAAAAYJYQPM1FPT3JmjVJd3eybFm9XLOmLgcAAACYJRa1uwJM06pVycqV9fC63l6hEwAAADDrCJ7msp4egRMAAAAwaxlqBwAAAEAjBE8AAAAANELwBAAAAEAjBE8AAAAANELwBAAAAEAjBE8AAAAANELwBAAAAEAjBE8AAAAANELwBAAAAEAjBE8AAAAANELwBAAAAEAjBE8AAAAANELwBAAAAEAjBE8AAAAANELwBAAAAEAjBE8AAAAANELwBAAAAEAjBE8AAAAANELwBAAAAEAjBE8AAAAANELwBAAAAEAjBE8AAAAANELwBAAAAEAjBE8AAAAANELwBAAAAEAjBE8AAAAANELwBAAAAEAjBE8AAAAANELwBAAAAEAjBE8AAAAANELwBAAAAEAjBE8AAAAANELwBAAAAEAjBE8AAAAANELwBAAAAEAjBE8AAAAANELwBAAAAEAjFrXjoKWU45Kck+TgJD9Pcl5VVRe1sN3RSc5OckSS7Ul+lOSFVVX9prnaAgAAADAdM97jqZTylCSXJflikicm+UiSC0spL5pkuxcm+XKSf05yZJInJbkgybYm6wsAAADA9LSjx9OpSa6uqurswcfXDYZRpyW5fKwNSikLk3w8yQVVVZ077KnrG6wnAAAAALugHXM8PS3J10aVXZnkyaWUznG2OSLJI5PcUkr5ZinltlLK1YND7wAAAACYhdrR4+mhSX41quzWJJ1JHpLkljG2efTg8r2pe0b9IMkrk3y1lLKiqqr/bPXg11xzzZQrPFts2LCh3VWAOUN7gdZpL9AabQVap71A6+Z7e2nL5OLT0DG4/NSwSch/UEp5dpI3Jvlfre5o+fLlWbx48e6uX+M2bNiQFStWtLsaMCdoL9A67QVao61A67QXaN18aC9btmyZsJNPO4ba3Zpk/1Fl+6eeJPz2cbYZ6gX101Hl16YeggcAAADALNOO4OnbSY4ZVXZsku9WVdU/zjYbktybpIwqPzjJxt1aOwAAAAB2i3YMtbsgybdKKWcl+XySo5O8KsnLhlYopbw4yXlJjq6q6qaqqn5bSvnLJG8tpfwo9RxPq5KsSHLSTJ8AAAAAAJOb8eCpqqrvlFJemuScJGck+UWSk6uqunzYavuk7t00/C53706yNclHkixL8uMkz6uq6sczUW8AAAAApqYtk4tXVXVFkismeP6SJJeMKtuW5PTBfwAAAADMcu2Y4wkAAACAPYDgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaMSiVlcspXQlOSLJIwaLfplkQ1VVW5uoGAAAAABz26TBUyllUZKzkpySZFmSjsGnBpL8tpTy8STvraqqv7FaMlJfX7JxY9Lbm/T0tLs2AAAAAGOacKjdYOj0lSRnJNmQ5NQkLx78d+pg2elJvjK4Lk1buzY58MDkmGPq5dq17a4RAAAAwJgmm+PplCRPT3JsVVUrk3w8yQVJbqiq6qNVVR2d5PlJnja4Lk3q60tWr042b042baqXq1fX5QAAAACzzGTB02uT/EVVVV8bfNyRpDfJ4qEVqqr6auow6rVNVJBhNm5MurpGlnV21uUAAAAAs8xkwdPBSa5qYT9XDa5Lk3p7k62j5nLv76/LAQAAAGaZyYKn/iR7tbCf7sF1aVJPT7JmTdLdnSxbVi/XrDHBOAAAADArTTYh+IYkr0w9wfhEXjW4Lk1btSpZudJd7QAAAIBZb7Lg6WNJLiul3JTknCR3J3lvkpuTpJSyNPUd71alvtMdM6GnR+AEAAAAzHoTDrWrqury1IHTu5LcmOTvkyxLclop5YuDZaclOaeqqi81W1UAAAAA5pLJejylqqozSyn/luSdSZ6fZMngU/cmuTrJn1VV9fXmqggAAADAXDRp8JQkg8HS10spC5M8eLD411VVbW+sZgAAAADMaS0FT0MGg6bbGqoLAAAAAPPIhHM8AQAAAMB0CZ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaMSidhy0lHJcknOSHJzk50nOq6rqoha3XZDka0mOTrKqqqq/bayiAAAAAEzbjPd4KqU8JcllSb6Y5IlJPpLkwlLKi1rcxR8n2dxM7QAAAADYXdrR4+nUJFdXVXX24OPrBsOo05JcPtGGpZRnJHljkiOS3NZgHQEAAADYRe2Y4+lpqYfKDXdlkieXUjrH26iU8qAkf5PkDVVV9TVYPwAAAAB2g3b0eHpokl+NKrs1SWeShyS5ZZztLk7yd1VVfX1XDn7NNdfsyuZttWHDhnZXAeYM7QVap71Aa7QVaJ32Aq2b7+2lLZOLT1Up5S1JDkjy8l3d1/Lly7N48eJdr9QM27BhQ1asWNHuasCcoL1A67QXaI22Aq3TXqB186G9bNmyZcJOPu0Yandrkv1Hle2fZFuS28fZZmWSw5PcU0rZVkrZNlj+N6WUHzZSSwAAAAB2STuCp28nOWZU2bFJvltVVf8427w19R3wDhv2L0neleRlu72GAAAAAOyydgy1uyDJt0opZyX5fJKjk7wqwwKkUsqLk5yX5Oiqqm6qqurno3dSSkmSX1ZVdcOM1BoAAACAKZnxHk9VVX0nyUuTvCLJj5O8I8nJVVVdPmy1fZKU1BOOAwAAADAHtWVy8aqqrkhyxQTPX5Lkkkn20bF7awUAAADA7tSOOZ4AAAAA2AMIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABohOAJAAAAgEYIngAAAABoxKJ2HLSUclySc5IcnOTnSc6rquqiCdZ/ZJIzkjwnyQFJbkvypSRnVVX1m+ZrDAAAAMBUzXiPp1LKU5JcluSLSZ6Y5CNJLiylvGiizZJ0JzklyfIkb0jy3CRrG60sAAAAANPWjh5Ppya5uqqqswcfXzcYRp2W5PKxNqiq6utJvj6s6L9KKaclubyUsqyqqjsbrC8AAAAA09COOZ6eluRro8quTPLkUkrnFPazb5ItSe7ZTfUCAAAAYDdqR4+nhyb51aiyW5N0JnlIklsm20Ep5SFJ3pfkwqqqtk3l4Ndcc81UVp9VNmzY0O4qwJyhvUDrtBdojbYCrdNeoHXzvb20ZXLxXVFK2TfJPyf5v0n+aKrbL1++PIsXL97d1Wrchg0bsmLFinZXA+YE7QVap71Aa7QVaJ32Aq2bD+1ly5YtE3byacdQu1uT7D+qbP8k25LcPtGGpZQHJ7kqyW+SHFdV1ZZGaggAAADALmtH8PTtJMeMKjs2yXerquofb6NSyv5J/jVJX+rQaXNjNQQAAABgl7VjqN0FSb5VSjkryeeTHJ3kVUleNrRCKeXFSc5LcnRVVTeVUn4nyb+k7ul0cpJ9SylDq/93VVVbZ7D+AAAAALRgxns8VVX1nSQvTfKKJD9O8o4kJ1dVdfmw1fZJUlJPOJ4kzx18/NQkG1NPQD7072kzUW8AAAAApqYtk4tXVXVFkismeP6SJJeM9xgAAACA2a8dczwBAAAAsAcQPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI0QPAEAAADQCMETAAAAAI1Y1I6DllKOS3JOkoOT/DzJeVVVXTTJNl1JPpjk1UkekORbSd5SVdV1DVd31rl947W57SffzX7/vyPzkN5D210dAAAAgDHNeI+nUspTklyW5ItJnpjkI0kuLKW8aJJN/zzJHyR5XZKnJLkryddLKUubq+3s860PnZK9Dn5cDnjp67PXwY/Lt/70lHZXCQAAAGBM7Rhqd2qSq6uqOruqquuqqvp4krVJThtvg1LKsiQnJfmjqqr+uaqqHyV5TZIHJ1k1E5WeDW7feG0OP/Pj2as/2WdLsld/cvgZH8/tG69td9UAAAAA7qcdQ+2eluQTo8quTHJxKaWzqqr+MbZ5UpKuJF8bKqiq6rellG8leXqSv2r14Ndcc83UazxLfOefPp9njIoK+xfU5Q996gvbUymYpTZs2NDuKsCcob1Aa7QVaJ32Aq2b7+2lHcHTQ5P8alTZrUk6kzwkyS3jbDOQ5LYxtnvYVA6+fPnyLF68eCqbzAobNmzIU17wP9N56ntHlHfuSJ7ygv9pricYZsOGDVmxYkW7qwFzgvYCrdFWoHXaC7RuPrSXLVu2TNjJx13t5pCH9B6aH3zgLbmnM9m0OLmnM/nBB94idAIAAABmpXb0eLo1yf6jyvZPsi3J7RNs05Fkv4zsEbV/kpt3dwVns6f/0cdy+yvetPOudk8XOgEAAACzVDt6PH07yTGjyo5N8t1x5ndKku8l2Tp8u8G72T09ybeaqORs9pDeQ/O4F7xOTycAAABgVmtHj6cLknyrlHJWks8nOTrJq5K8bGiFUsqLk5yX5Oiqqm6qqurOUsqFST5USrktdS+n9yb579R3xAMAAABglpnxHk9VVX0nyUuTvCLJj5O8I8nJVVVdPmy1fZKU1BOOD3lHkr9NcmmS7yRZluSYqqrumoFqAwAAADBF7ejxlKqqrkhyxQTPX5LkklFlW5OcOvgPAAAAgFnOXe0AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGLGp3BWbQwiTZunVru+sxbVu2bGl3FWDO0F6gddoLtEZbgdZpL9C6ud5ehuUsC8d6vmNgYGDmatNGGzZseEaSq9tdDwAAAIB56PdWrFjxzdGFe1KPp/VJfi/JLUm2t7kuAAAAAPPBwiQPS5273M8e0+MJAAAAgJllcnEAAAAAGiF4AgAAAKARgicAAAAAGiF4AgAAAKARgicAAAAAGiF4AgAAAKARgicAAAAAGiF4AgAAAKARi9pdASZWSjkuyTlJDk7y8yTnVVV1UXtrBTOrlHJUktOSrEjysCSrqqr621HrPC3JBUmemKQvyf+uquq8UescmuTjSZ6W5LdJLk3y7qqq+hs/CZgBpZQ/SvLSJIck2Z7k+0nOrKrq30etN+nvllbaFMxlpZSTkrwpyUFJFia5IckFVVV9etg62gqMUkp5bZJPJ/lqVVXHDivXXtjjlVLOTvKeMZ7qrKpq2+A6e1xb0eNpFiulPCXJZUm+mPoD95EkF5ZSXtTOekEb7J3kP5O8eawnSykHJvlakh8kOSLJHyU5s5Tyh8PWeUCSdUk2JXlKktcneU2SP22y4jDDnpXkwiS/l+Tpqb/MfL2U8pihFVr53dJKm4J54OYkZyY5MnVbuDjJmlLKCxNtBcZSSilJPpjk30aVay9wnxtS/7F8579hodMe2VY6BgYG2l0HxlFK+dsk+1dV9exhZZcmeVRVVU9vX82gfUopAxnV46mU8sEkr0jy6KqqBgbL3p/khCSPqKpqoJTyxiR/lrpN3T24zuokf5mkp6qq387wqUDjSikLktyW5L1VVX1ssGzS3y2ttKmZPROYGaWU7ye5qqqq07QVGKmUsjjJd1J/n1qZ5KFDPZ60F6gN9nh6ZVVVh4zz/B7ZVvR4mt2eljrpHO7KJE8upXS2oT4wWz0tyddH/RC+MskBSQ4cts63hkKnYessTj2ED+ajJak/478ZVtbK75ZW2hTMG6WUBaWUY5KUJP86WKytwEh/keTHVVV9dozntBe4z4GllF+WUm4spVxeSnnCsOf2yLYieJrdHprkV6PKbk3SmeQhM18dmLXGaytJ3b11vHV+lWRg2Dow35yf5I4kVwwra+V3SyttCua8UsojSyl3JdmS5EtJTqmq6p8Gn9ZWYFAp5SVJnpd6XrSxaC9Q+07qnkm/n3pqj0VJvj1s2oM9sq2YXBwA5qFSyhlJXpVkpaGkMK6bkxyWZGnqoUMXlFJuqqrqq22tFcwipZRHJPlkkhf6fQITq6rqn4c/LqV8M8mPk5ySZM7O0bSrBE+z261J9h9Vtn+SbUlun/nqwKw1XltJklsmWGe/JB3D1oF5oZTy3iRvTXJMVVUbRj3dyu+WVtoUzHmDk73eMPjwh6WUg5K8N8lXo63AkBVJepJ8q55bPMngyJlSyrbUN23RXmAMVVX1l1LWp76DXbKHthVD7Wa3byc5ZlTZsUm+6/bvMMK3U/+lerhjk9yU5MZh6zy9lLLXqHW2JBl9YQ5zVinlQ6n/qrayqqrvjbFKK79bWmlTMB8tSD03WqKtwJCrkjw+de/AoX9fSv35PyzJT6K9wJgGb/RyWO4LjPbItqLH0+x2Qeq/LJyV5PNJjk49bOJlba0VzLBSytIkjxlWdGAp5bAkd1VVdUOSTyR5Synlfyf5WOpbk74jyZ8Mm5Tvb5KcleTSwbtNHJDkvCSf1G2c+aKU8rHU8wq8LMlNpZSHDj61uaqqTYP/b+V3SyttCua0Usq5qSd4vTF12PT8JKuTvHtwFW0Fkgx+T7pmeFkp5Y4kS6qqumbwsfYCSUopf57ky6l/tzw49Wf84NTzPSV76O8WPZ5msaqqvpPkpalvpfjj1B+2k6uquryd9YI2eFKSHwz+S5IPDv7/r5OkqqobU094+aQkP0x9m99zqqr68NAOBr80rUyyb5LvJrk0dRh12gzUH2bKW5LsneSfU/9lbejfR4ZWaOV3SyttCuaBBye5KMm1Sb6Z5OVJVldV9ReJtgJTob3ATr+T5LNJrksdQC1L8vShqQ/21LbSMTAwJwMzAAAAAGY5PZ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQAAAIBGCJ4AAAAAaITgCQBgD1VK6Sql7NPuegAA89eidlcAAICZMRgyvSPJi5McmOQBSb6fZEU76wUAzF+CJwBg1imlnJ3kPcOK/jvJtUnOq6rqn9pSqTmulPKQJN9KsneSc1IHTtuT/Kqd9QIA5jfBEwAwW21P8ozB/++fuqfOP5ZSnltV1dfbV60568+S7JXkKVVV3dTuygAAewbBEwAwa1VV9R9D/y+lfCPJz5O8NYngaQpKKcuSvCrJaqETADCTBE8AwJxQVdWdpZTrkzwqSUop3Uk+mOSY1PMV/Sb1ULJ3VlX1i6HtSimfTvKcJPsl2ZTk/wyuc+Pg82enHtb3g6qqjhh+zFLK95McnuTMqqo+MKx8ZZKzUs+NtC11EHbqqOMODO53SZLVSZYmuTLJm6qqGnd4WymlN8nPhhXdm+T6JOdWVfX5UfsfUa9R+9mY5JKqqs5O8tgknUn2K6VsSHJIkt8muSzJu6uqunPYdvumfl1fnGTfwWP/aVVVlw5b5+wkZyR5cpL/neSwJDcneX9VVZcMW++SJL1VVT1r8HFXki8P1ucZVVXdXEp5VpJvJDmoqqqNg+sdMHjcvaqq6hjvtQIAZj93tQMA5oRSyqIkj0gy1GOnO3Woc3aS30/y9iSPTPLNwVBqyDeTvDnJ0UneOLjO5zPSjiSPKqU8adjxnpykd4x6HJfkq6mDllckOTHJ45J8o5Sy96jVT0odzqxO3VPrmUn+ocVTfnOS301yfJJbknyulHJoi9uOtnRw+WdJ/jPJS1PP8/Sa1MMXFyRJKWVhkq8kWZXk/YPH/n6Sz5RSThy1z44kf5/k71KHVN9NcnEp5XljVWBw359LsjzJyqqqbp6gvuclWTzFcwQAZiE9ngCAWWswbErq3kpnJXno4DJVVf13kpOHrbswydWpA6Hnp+7Nk6qq/mowWFmQpCfJDUn+x6hDDST5m9RB0fcGy05KcmnqwGi4C5L8c1VVrxx27PVJqiSvTfKJYesuSPI/qqraMrheX5IrBuep+tokp//ToaGGpZRfJvlJ6tDm2km2G8tQr6GvVFX1hsH/X1lKuT11GPTc1L2xfj912PWKqqq+MGy9hyZ5bynlr6uqGhh2bh+uqupjw9Y7OHUvr68OP3gppSPJXyV5VpJnVlX1/8ar6GDg9weD65883noAwNygxxMAMFstTNI/+O+mJK9OcnZVVX81tEIpZVUpZX0p5c7UQ96GetEcPGpfZw3uZ6iX0ntyfxcmWVVKWVpKWZrklYNlO5VSHpt6qN/nSimLhv4l+UWS65L83qh9fnkodBp6nGRLkqe2cv6D+39Q6hBsc+peRcMtGFyn1e90nxv1+AupX7ejBh8flWRrki+OWm9tkoelHiI33N+PevzFJE8aDAGH+4skJyR5e1VVP5mkjhek7pH2H5OsBwDMAYInAGC22p56mNqTkjw6yb5VVb136MlSyvGpg5RrU/eQ+d0kT0nde2nJqH1dOPjcqtQ9oX44+mBVVf3n4L5WpZ6I+8djhCT7DS7/JveFYkP/Hp/kwaPWv23UMQaS9KUOcSazbnC/v07d8+eUoXmphnnv4Dr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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "K = 500\n", "N = 500\n", " \n", "linspace = np.linspace(1, N, N)\n", "sample = sps.norm(0, 1).rvs(K * N).reshape(K, N)\n", " \n", "sample_sq = sample * sample\n", "# найдем X среднее для выборки размера 1,2,3.., возведем в квадрат\n", "sample = np.cumsum(sample, axis=1)\n", "sample /= linspace\n", "sample_av_sq = sample * sample\n", "\n", "# найдем X^2 среднее\n", "sample_sq_av = np.cumsum(sample_sq, axis=1)\n", "sample_sq_av /= linspace\n", "# # получаем набор тет\n", "teta_disp_n = sample_sq_av - sample_av_sq\n", "\n", "# на 0 не будем делить 2 точки проблемы не сделают\n", "linspace[0] = 2\n", "teta_n = teta_disp_n * linspace / (linspace - 1)\n", " \n", "# усредняем\n", "teta = teta_n.mean(axis=0)\n", "teta_disp = teta_disp_n.mean(axis=0)\n", "\n", "#отрисовка\n", "plt.figure(figsize=(20, 10))\n", "\n", "plt.scatter(linspace, teta,\n", " alpha=1, s=20, color='green', label='n/(n-1) * S^2')\n", "\n", "plt.scatter(linspace, teta_disp,\n", " alpha=1, s=20, color='red', label='S^2')\n", "\n", "\n", "plt.xlabel(\"Размер выборки\")\n", "plt.ylabel(\"$\\Theta$\")\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "agviQcZi3jvd" }, "source": [ "Сделайте вывод о том, что такое свойство несмещенности. Подтверждают ли сделанные эксперименты полученное в теоретических задачах свойство несмещенности (или отсутствие этого свойства) данных оценок? Поясните, почему в лабораторных по физике при оценке погрешности иногда используют $n-1$ в знаменателе, а не $n$." ] }, { "cell_type": "markdown", "metadata": { "id": "md1nb2PX3jvd" }, "source": [ "**Вывод:** \n", "Видно, что при достаточно больших $n$ оценки очень близки. $\\overline{\\theta}$ совсем не колеблется в обоих случаях.\n", "Опираясь на лабораторные работы по физике можно сказать, что несмещенность оценки говорит об отсутствии систематической ошибки. А свойство состоятельности обеспечиввает близость оценки и величины при увеличении числа измерений. Видно, что при увеличении числа измерений обе оценки прибоижаются к реальному значению $\\sigma^2$ они являются состоятельными, но несмещенная оценка делает это быстрее.\n", "Поэтому в лабах, где нет возможности проводить эксперимент бесконечность раз, используют несмещенную оценку, дабы получить лучше результат.\n", "Собственно, поэтому часто используется $n-1$ в знаменателе." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Замечание.* Проведенные эксперименты позволяют сделать вывод только о поведении среднего значения оценки, но ничего не говорят о том, насколько велик их разброс относительно среднего." ] }, { "attachments": { "a47371.buy12r.1ets.rs.ii.jpg": { "image/jpeg": 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} }, "cell_type": "markdown", "metadata": {}, "source": [ " ---\n", "### Задача 3. \n", "\n", "В этой задаче вам нужно будет проанализировать вакансии на сайте hh.ru. Для сбора данных рекомендуется использовать официальное API.\n", "\n", "![a47371.buy12r.1ets.rs.ii.jpg](attachment:a47371.buy12r.1ets.rs.ii.jpg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**1. Сбор данных**\n", "\n", "Соберите данные о вакансии Data Scientist по Москве в одну таблицу и подготовьте их для анализа. Часто в индустрии подготовленные для анализа таблицы называют *витринами*. В ней должны быть:\n", "- название вакансии,\n", "- работодатель,\n", "- зарплатная вилка,\n", "- станция метро,\n", "- ссылка на вакансию,\n", "- опыт,\n", "- требуемые навыки,\n", "- что-то еще по вашему желанию.\n", "\n", "Креативность приветствуется. При работе с данными о заработной плате обратите внимание на валюту и gross/net. \n", "\n", "**При работе с API не забывайте делать паузы между запросами, чтобы не задудосить сервер HeadHunter.**\n", "\n", "Пример запроса к API:" ] }, { "cell_type": "code", "execution_count": 1297, "metadata": {}, "outputs": [], "source": [ "# пример\n", "URL = 'https://api.hh.ru/vacancies'\n", "\n", "params = {\n", " 'text': \"Повар\",\n", " 'area': 1,\n", " 'page': 0,\n", " 'per_page': 10\n", "}\n", "\n", "req = requests.get(URL, params)\n", "data = json.loads(req.content.decode())" ] }, { "cell_type": "code", "execution_count": 1298, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'items': [{'id': '44167231',\n", " 'premium': False,\n", " 'name': 'Бренд Шеф-повар',\n", " 'department': None,\n", " 'has_test': False,\n", " 'response_letter_required': False,\n", " 'area': {'id': '1', 'name': 'Москва', 'url': 'https://api.hh.ru/areas/1'},\n", " 'salary': {'from': 350000, 'to': None, 'currency': 'RUR', 'gross': False},\n", " 'type': {'id': 'open', 'name': 'Открытая'},\n", " 'address': None,\n", " 'response_url': None,\n", " 'sort_point_distance': None,\n", " 'published_at': '2021-04-25T15:43:45+0300',\n", " 'created_at': '2021-04-25T15:43:45+0300',\n", " 'archived': False,\n", " 'apply_alternate_url': 'https://hh.ru/applicant/vacancy_response?vacancyId=44167231',\n", " 'insider_interview': None,\n", " 'url': 'https://api.hh.ru/vacancies/44167231?host=hh.ru',\n", " 'alternate_url': 'https://hh.ru/vacancy/44167231',\n", " 'relations': [],\n", " 'employer': {'id': '2722950',\n", " 'name': 'Фуд-холл Гоголь-Моголь',\n", " 'url': 'https://api.hh.ru/employers/2722950',\n", " 'alternate_url': 'https://hh.ru/employer/2722950',\n", " 'logo_urls': None,\n", " 'vacancies_url': 'https://api.hh.ru/vacancies?employer_id=2722950',\n", " 'trusted': True},\n", " 'snippet': {'requirement': 'Продолжительный (не менее 3х лет на одном месте) опыт работы в известных ресторанах на должности Шефа/Бренд-шефа обязателен. ',\n", " 'responsibility': 'Разработка меню разных направлений а-ля карт, кулинария, гриль, паназия.'},\n", " 'contacts': None,\n", " 'schedule': {'id': 'fullDay', 'name': 'Полный день'},\n", " 'working_days': [],\n", " 'working_time_intervals': [],\n", " 'working_time_modes': [],\n", " 'accept_temporary': False},\n", " {'id': '43428486',\n", " 'premium': False,\n", " 'name': 'Повар универсал в ресторан премиум класса',\n", " 'department': None,\n", " 'has_test': False,\n", " 'response_letter_required': False,\n", " 'area': {'id': '1', 'name': 'Москва', 'url': 'https://api.hh.ru/areas/1'},\n", " 'salary': {'from': 100000, 'to': None, 'currency': 'RUR', 'gross': False},\n", " 'type': {'id': 'open', 'name': 'Открытая'},\n", " 'address': {'city': 'Москва',\n", " 'street': 'Ружейный переулок',\n", " 'building': '6с2',\n", " 'description': None,\n", " 'lat': 55.743786,\n", " 'lng': 37.580921,\n", " 'raw': 'Москва, Ружейный переулок, 6с2',\n", " 'metro': {'station_name': 'Смоленская',\n", " 'line_name': 'Арбатско-Покровская',\n", " 'station_id': '3.131',\n", " 'line_id': '3',\n", " 'lat': 55.747713,\n", " 'lng': 37.583802},\n", " 'metro_stations': [{'station_name': 'Смоленская',\n", " 'line_name': 'Арбатско-Покровская',\n", " 'station_id': '3.131',\n", " 'line_id': '3',\n", " 'lat': 55.747713,\n", " 'lng': 37.583802},\n", " {'station_name': 'Смоленская',\n", " 'line_name': 'Филевская',\n", " 'station_id': '4.132',\n", " 'line_id': '4',\n", " 'lat': 55.749083,\n", " 'lng': 37.582173}],\n", " 'id': '5502923'},\n", " 'response_url': None,\n", " 'sort_point_distance': None,\n", " 'published_at': '2021-04-25T14:58:46+0300',\n", " 'created_at': '2021-04-25T14:58:46+0300',\n", " 'archived': False,\n", " 'apply_alternate_url': 'https://hh.ru/applicant/vacancy_response?vacancyId=43428486',\n", " 'insider_interview': None,\n", " 'url': 'https://api.hh.ru/vacancies/43428486?host=hh.ru',\n", " 'alternate_url': 'https://hh.ru/vacancy/43428486',\n", " 'relations': [],\n", " 'employer': {'id': '2841043',\n", " 'name': 'Рико',\n", " 'url': 'https://api.hh.ru/employers/2841043',\n", " 'alternate_url': 'https://hh.ru/employer/2841043',\n", " 'logo_urls': {'90': 'https://hhcdn.ru/employer-logo/2221831.jpeg',\n", " '240': 'https://hhcdn.ru/employer-logo/2221832.jpeg',\n", " 'original': 'https://hhcdn.ru/employer-logo-original/444966.jpg'},\n", " 'vacancies_url': 'https://api.hh.ru/vacancies?employer_id=2841043',\n", " 'trusted': True},\n", " 'snippet': {'requirement': 'Аккуратность, чистоплотность. Трудолюбие. Обучаемость. Умение работать в команде. Вежливость, грамотная речь.',\n", " 'responsibility': 'Работа в холодном цеху. Работа в горячем цеху. Выпечка хлеба и десертов. Приемка продуктов по качеству и количеству. Инвентаризация. '},\n", " 'contacts': None,\n", " 'schedule': {'id': 'fullDay', 'name': 'Полный день'},\n", " 'working_days': [],\n", " 'working_time_intervals': [],\n", " 'working_time_modes': [],\n", " 'accept_temporary': False},\n", " {'id': '43970396',\n", " 'premium': False,\n", " 'name': 'Повар в ресторан Премиум класса',\n", " 'department': None,\n", " 'has_test': False,\n", " 'response_letter_required': False,\n", " 'area': {'id': '1', 'name': 'Москва', 'url': 'https://api.hh.ru/areas/1'},\n", " 'salary': {'from': 55000, 'to': 80000, 'currency': 'RUR', 'gross': False},\n", " 'type': {'id': 'open', 'name': 'Открытая'},\n", " 'address': {'city': 'Москва',\n", " 'street': 'Трёхпрудный переулок',\n", " 'building': '15',\n", " 'description': None,\n", " 'lat': 55.766081,\n", " 'lng': 37.595591,\n", " 'raw': 'Москва, Трёхпрудный переулок, 15',\n", " 'metro': None,\n", " 'metro_stations': [],\n", " 'id': '5441283'},\n", " 'response_url': None,\n", " 'sort_point_distance': None,\n", " 'published_at': '2021-04-23T19:37:31+0300',\n", " 'created_at': '2021-04-23T19:37:31+0300',\n", " 'archived': False,\n", " 'apply_alternate_url': 'https://hh.ru/applicant/vacancy_response?vacancyId=43970396',\n", " 'insider_interview': None,\n", " 'url': 'https://api.hh.ru/vacancies/43970396?host=hh.ru',\n", " 'alternate_url': 'https://hh.ru/vacancy/43970396',\n", " 'relations': [],\n", " 'employer': {'id': '5284457',\n", " 'name': 'Butler',\n", " 'url': 'https://api.hh.ru/employers/5284457',\n", " 'alternate_url': 'https://hh.ru/employer/5284457',\n", " 'logo_urls': {'90': 'https://hhcdn.ru/employer-logo/3755815.jpeg',\n", " '240': 'https://hhcdn.ru/employer-logo/3755816.jpeg',\n", " 'original': 'https://hhcdn.ru/employer-logo-original/828733.jpg'},\n", " 'vacancies_url': 'https://api.hh.ru/vacancies?employer_id=5284457',\n", " 'trusted': True},\n", " 'snippet': {'requirement': 'Опыт работы в ресторанах Премиум класса от 4-х лет (Итальянская, Средиземноморская, Европейская кухня). Ответственность, профессиональный подход к работе. ',\n", " 'responsibility': 'Приготовление блюд, отдача в соответствии с технологическими картами. Контроль качества используемых продуктов. Контроль за свежестью, кондицией продуктов и соответствие их...'},\n", " 'contacts': None,\n", " 'schedule': {'id': 'fullDay', 'name': 'Полный день'},\n", " 'working_days': [],\n", " 'working_time_intervals': [],\n", " 'working_time_modes': [],\n", " 'accept_temporary': False},\n", " {'id': '43821452',\n", " 'premium': False,\n", " 'name': 'Повар-универсал',\n", " 'department': None,\n", " 'has_test': False,\n", " 'response_letter_required': False,\n", " 'area': {'id': '1', 'name': 'Москва', 'url': 'https://api.hh.ru/areas/1'},\n", " 'salary': {'from': 70000, 'to': None, 'currency': 'RUR', 'gross': False},\n", " 'type': {'id': 'open', 'name': 'Открытая'},\n", " 'address': None,\n", " 'response_url': None,\n", " 'sort_point_distance': None,\n", " 'published_at': '2021-04-24T18:30:59+0300',\n", " 'created_at': '2021-04-24T18:30:59+0300',\n", " 'archived': False,\n", " 'apply_alternate_url': 'https://hh.ru/applicant/vacancy_response?vacancyId=43821452',\n", " 'insider_interview': None,\n", " 'url': 'https://api.hh.ru/vacancies/43821452?host=hh.ru',\n", " 'alternate_url': 'https://hh.ru/vacancy/43821452',\n", " 'relations': [],\n", " 'employer': {'id': '4896197',\n", " 'name': 'Под небом',\n", " 'url': 'https://api.hh.ru/employers/4896197',\n", " 'alternate_url': 'https://hh.ru/employer/4896197',\n", " 'logo_urls': {'original': 'https://hhcdn.ru/employer-logo-original/773301.png',\n", " '90': 'https://hhcdn.ru/employer-logo/3534095.png',\n", " '240': 'https://hhcdn.ru/employer-logo/3534096.png'},\n", " 'vacancies_url': 'https://api.hh.ru/vacancies?employer_id=4896197',\n", " 'trusted': True},\n", " 'snippet': {'requirement': 'Опыт работы поваром. - Мед. книжка. - Аккуратность, ответственность.',\n", " 'responsibility': 'Приготовление блюд. - Заготовки. - Поддержание порядка на рабочем месте.'},\n", " 'contacts': None,\n", " 'schedule': {'id': 'shift', 'name': 'Сменный график'},\n", " 'working_days': [],\n", " 'working_time_intervals': [],\n", " 'working_time_modes': [],\n", " 'accept_temporary': True},\n", " {'id': '44168751',\n", " 'premium': True,\n", " 'name': 'Су-шеф, Шеф-повар',\n", " 'department': None,\n", " 'has_test': False,\n", " 'response_letter_required': False,\n", " 'area': {'id': '1', 'name': 'Москва', 'url': 'https://api.hh.ru/areas/1'},\n", " 'salary': {'from': 60000, 'to': 80000, 'currency': 'RUR', 'gross': False},\n", " 'type': {'id': 'open', 'name': 'Открытая'},\n", " 'address': {'city': 'Москва',\n", " 'street': 'Мосфильмовская улица',\n", " 'building': '88к2с5',\n", " 'description': None,\n", " 'lat': 55.7049,\n", " 'lng': 37.492105,\n", " 'raw': 'Москва, Мосфильмовская улица, 88к2с5',\n", " 'metro': None,\n", " 'metro_stations': [],\n", " 'id': '5664083'},\n", " 'response_url': None,\n", " 'sort_point_distance': None,\n", " 'published_at': '2021-04-25T21:11:49+0300',\n", " 'created_at': '2021-04-25T21:11:49+0300',\n", " 'archived': False,\n", " 'apply_alternate_url': 'https://hh.ru/applicant/vacancy_response?vacancyId=44168751',\n", " 'insider_interview': None,\n", " 'url': 'https://api.hh.ru/vacancies/44168751?host=hh.ru',\n", " 'alternate_url': 'https://hh.ru/vacancy/44168751',\n", " 'relations': [],\n", " 'employer': {'id': '3739477',\n", " 'name': 'I Do The Kitchen',\n", " 'url': 'https://api.hh.ru/employers/3739477',\n", " 'alternate_url': 'https://hh.ru/employer/3739477',\n", " 'logo_urls': {'original': 'https://hhcdn.ru/employer-logo-original/792832.JPG',\n", " '90': 'https://hhcdn.ru/employer-logo/3612215.jpeg',\n", " '240': 'https://hhcdn.ru/employer-logo/3612216.jpeg'},\n", " 'vacancies_url': 'https://api.hh.ru/vacancies?employer_id=3739477',\n", " 'trusted': True},\n", " 'snippet': {'requirement': 'СТРОГО С ОПЫТОМ РАБОТЫ НА ДОЛЖНОСТИ СУ-ШЕФА от 1-го года в кафе и ресторанах Москвы. Иметь стиль и...',\n", " 'responsibility': 'Обеспечение продуктивной и бесперебойной работы кухни. Управление персоналом на смене. Контроль технологии приготовления и качества отдачи блюд. Контроль остатков, списания...'},\n", " 'contacts': None,\n", " 'schedule': {'id': 'fullDay', 'name': 'Полный день'},\n", " 'working_days': [],\n", " 'working_time_intervals': [],\n", " 'working_time_modes': [],\n", " 'accept_temporary': False},\n", " {'id': '43393079',\n", " 'premium': False,\n", " 'name': 'Повар',\n", " 'department': None,\n", " 'has_test': False,\n", " 'response_letter_required': False,\n", " 'area': {'id': '1', 'name': 'Москва', 'url': 'https://api.hh.ru/areas/1'},\n", " 'salary': {'from': 110000, 'to': 130000, 'currency': 'RUR', 'gross': True},\n", " 'type': {'id': 'open', 'name': 'Открытая'},\n", " 'address': None,\n", " 'response_url': None,\n", " 'sort_point_distance': None,\n", " 'published_at': '2021-04-23T11:01:46+0300',\n", " 'created_at': '2021-04-23T11:01:46+0300',\n", " 'archived': False,\n", " 'apply_alternate_url': 'https://hh.ru/applicant/vacancy_response?vacancyId=43393079',\n", " 'insider_interview': None,\n", " 'url': 'https://api.hh.ru/vacancies/43393079?host=hh.ru',\n", " 'alternate_url': 'https://hh.ru/vacancy/43393079',\n", " 'relations': [],\n", " 'employer': {'id': '4514579',\n", " 'name': 'HULAB',\n", " 'url': 'https://api.hh.ru/employers/4514579',\n", " 'alternate_url': 'https://hh.ru/employer/4514579',\n", " 'logo_urls': {'90': 'https://hhcdn.ru/employer-logo/3384457.jpeg',\n", " '240': 'https://hhcdn.ru/employer-logo/3384458.jpeg',\n", " 'original': 'https://hhcdn.ru/employer-logo-original/735870.jpeg'},\n", " 'vacancies_url': 'https://api.hh.ru/vacancies?employer_id=4514579',\n", " 'trusted': True},\n", " 'snippet': {'requirement': 'Знание европейской , кавказской , итальянской кухни . Знание детского меню.',\n", " 'responsibility': 'Необходимо уметь приготовить вкусную домашнюю еду (чтобы повар мог приготовить вкусные шашлыки на мангале, испечь пирожки и тд).'},\n", " 'contacts': None,\n", " 'schedule': {'id': 'fullDay', 'name': 'Полный день'},\n", " 'working_days': [],\n", " 'working_time_intervals': [],\n", " 'working_time_modes': [],\n", " 'accept_temporary': False},\n", " {'id': '43685445',\n", " 'premium': False,\n", " 'name': 'Шеф-повар',\n", " 'department': None,\n", " 'has_test': False,\n", " 'response_letter_required': False,\n", " 'area': {'id': '1', 'name': 'Москва', 'url': 'https://api.hh.ru/areas/1'},\n", " 'salary': {'from': 120000, 'to': None, 'currency': 'RUR', 'gross': False},\n", " 'type': {'id': 'open', 'name': 'Открытая'},\n", " 'address': {'city': 'Камчатский край',\n", " 'street': None,\n", " 'building': None,\n", " 'description': None,\n", " 'lat': 61.350179,\n", " 'lng': 169.782981,\n", " 'raw': 'Камчатский край',\n", " 'metro': None,\n", " 'metro_stations': [],\n", " 'id': '3981983'},\n", " 'response_url': None,\n", " 'sort_point_distance': None,\n", " 'published_at': '2021-04-25T13:12:34+0300',\n", " 'created_at': '2021-04-25T13:12:34+0300',\n", " 'archived': False,\n", " 'apply_alternate_url': 'https://hh.ru/applicant/vacancy_response?vacancyId=43685445',\n", " 'insider_interview': None,\n", " 'url': 'https://api.hh.ru/vacancies/43685445?host=hh.ru',\n", " 'alternate_url': 'https://hh.ru/vacancy/43685445',\n", " 'relations': [],\n", " 'employer': {'id': '4518101',\n", " 'name': 'Архитектурно-строительная компания FANTALIS',\n", " 'url': 'https://api.hh.ru/employers/4518101',\n", " 'alternate_url': 'https://hh.ru/employer/4518101',\n", " 'logo_urls': {'90': 'https://hhcdn.ru/employer-logo/3783511.jpeg',\n", " '240': 'https://hhcdn.ru/employer-logo/3783512.jpeg',\n", " 'original': 'https://hhcdn.ru/employer-logo-original/835657.jpg'},\n", " 'vacancies_url': 'https://api.hh.ru/vacancies?employer_id=4518101',\n", " 'trusted': True},\n", " 'snippet': {'requirement': 'С покладистым характером и умением работы с большим коллективом поваров до 35 чел. Заинтересованного в долгосрочном сотрудничестве. С опытом работы...',\n", " 'responsibility': 'Координация деятельности работы кухни. Контроль качества сырья и готовой продукции. Организация рабочего процесса производства, распределение обязанностей и контроль их выполнения. '},\n", " 'contacts': None,\n", " 'schedule': {'id': 'flyInFlyOut', 'name': 'Вахтовый метод'},\n", " 'working_days': [],\n", " 'working_time_intervals': [],\n", " 'working_time_modes': [],\n", " 'accept_temporary': False},\n", " {'id': '43686573',\n", " 'premium': False,\n", " 'name': 'Повар',\n", " 'department': None,\n", " 'has_test': False,\n", " 'response_letter_required': True,\n", " 'area': {'id': '1', 'name': 'Москва', 'url': 'https://api.hh.ru/areas/1'},\n", " 'salary': {'from': 200000, 'to': 250000, 'currency': 'RUR', 'gross': False},\n", " 'type': {'id': 'open', 'name': 'Открытая'},\n", " 'address': None,\n", " 'response_url': None,\n", " 'sort_point_distance': None,\n", " 'published_at': '2021-04-25T13:31:55+0300',\n", " 'created_at': '2021-04-25T13:31:55+0300',\n", " 'archived': False,\n", " 'apply_alternate_url': 'https://hh.ru/applicant/vacancy_response?vacancyId=43686573',\n", " 'insider_interview': None,\n", " 'url': 'https://api.hh.ru/vacancies/43686573?host=hh.ru',\n", " 'alternate_url': 'https://hh.ru/vacancy/43686573',\n", " 'relations': [],\n", " 'employer': {'id': '5333735',\n", " 'name': 'Яковлева Вероника',\n", " 'url': 'https://api.hh.ru/employers/5333735',\n", " 'alternate_url': 'https://hh.ru/employer/5333735',\n", " 'logo_urls': None,\n", " 'vacancies_url': 'https://api.hh.ru/vacancies?employer_id=5333735',\n", " 'trusted': False},\n", " 'snippet': {'requirement': 'База поставщиков продуктов. Уверенный прошлый опыт. Итальянская и Пан азиатская кухни.',\n", " 'responsibility': 'Готовить и сервировать блюда, ресторанная подача блюд. Закупка продуктов. Обслуживание семейных мероприятий ( не часто).'},\n", " 'contacts': None,\n", " 'schedule': {'id': 'flexible', 'name': 'Гибкий график'},\n", " 'working_days': [],\n", " 'working_time_intervals': [],\n", " 'working_time_modes': [],\n", " 'accept_temporary': False},\n", " {'id': '43423271',\n", " 'premium': False,\n", " 'name': 'Повар-универсал в кафе с завтраками',\n", " 'department': None,\n", " 'has_test': False,\n", " 'response_letter_required': False,\n", " 'area': {'id': '1', 'name': 'Москва', 'url': 'https://api.hh.ru/areas/1'},\n", " 'salary': {'from': 50000, 'to': 70000, 'currency': 'RUR', 'gross': False},\n", " 'type': {'id': 'open', 'name': 'Открытая'},\n", " 'address': {'city': 'Москва',\n", " 'street': 'Лубянский проезд',\n", " 'building': '25с2',\n", " 'description': None,\n", " 'lat': 55.754578,\n", " 'lng': 37.63482,\n", " 'raw': 'Москва, Лубянский проезд, 25с2',\n", " 'metro': None,\n", " 'metro_stations': [],\n", " 'id': '715797'},\n", " 'response_url': None,\n", " 'sort_point_distance': None,\n", " 'published_at': '2021-04-25T13:09:09+0300',\n", " 'created_at': '2021-04-25T13:09:09+0300',\n", " 'archived': False,\n", " 'apply_alternate_url': 'https://hh.ru/applicant/vacancy_response?vacancyId=43423271',\n", " 'insider_interview': None,\n", " 'url': 'https://api.hh.ru/vacancies/43423271?host=hh.ru',\n", " 'alternate_url': 'https://hh.ru/vacancy/43423271',\n", " 'relations': [],\n", " 'employer': {'id': '2375473',\n", " 'name': 'Белмол',\n", " 'url': 'https://api.hh.ru/employers/2375473',\n", " 'alternate_url': 'https://hh.ru/employer/2375473',\n", " 'logo_urls': {'240': 'https://hhcdn.ru/employer-logo/3419382.jpeg',\n", " '90': 'https://hhcdn.ru/employer-logo/3419381.jpeg',\n", " 'original': 'https://hhcdn.ru/employer-logo-original/744605.JPG'},\n", " 'vacancies_url': 'https://api.hh.ru/vacancies?employer_id=2375473',\n", " 'trusted': True},\n", " 'snippet': {'requirement': 'Наличие профильного образования или опыта работы - желательно. Наличие мед.книжки или готовность ее оформить. Аккуратность, чистоплотность, не конфликтность.',\n", " 'responsibility': 'Приготовление блюд и заготовок согласно технологическим картам. Организация и чистота рабочего места. Участие в инвентаризациях и приеме поставок (но у...'},\n", " 'contacts': None,\n", " 'schedule': {'id': 'fullDay', 'name': 'Полный день'},\n", " 'working_days': [],\n", " 'working_time_intervals': [],\n", " 'working_time_modes': [],\n", " 'accept_temporary': False},\n", " {'id': '44120403',\n", " 'premium': False,\n", " 'name': 'Повар к одной женщине',\n", " 'department': None,\n", " 'has_test': False,\n", " 'response_letter_required': False,\n", " 'area': {'id': '1', 'name': 'Москва', 'url': 'https://api.hh.ru/areas/1'},\n", " 'salary': {'from': 90000, 'to': 100000, 'currency': 'RUR', 'gross': False},\n", " 'type': {'id': 'open', 'name': 'Открытая'},\n", " 'address': None,\n", " 'response_url': None,\n", " 'sort_point_distance': None,\n", " 'published_at': '2021-04-22T16:11:07+0300',\n", " 'created_at': '2021-04-22T16:11:07+0300',\n", " 'archived': False,\n", " 'apply_alternate_url': 'https://hh.ru/applicant/vacancy_response?vacancyId=44120403',\n", " 'insider_interview': None,\n", " 'url': 'https://api.hh.ru/vacancies/44120403?host=hh.ru',\n", " 'alternate_url': 'https://hh.ru/vacancy/44120403',\n", " 'relations': [],\n", " 'employer': {'id': '1175930',\n", " 'name': 'Адам и Ева и К',\n", " 'url': 'https://api.hh.ru/employers/1175930',\n", " 'alternate_url': 'https://hh.ru/employer/1175930',\n", " 'logo_urls': {'90': 'https://hhcdn.ru/employer-logo/915014.jpeg',\n", " '240': 'https://hhcdn.ru/employer-logo/915015.jpeg',\n", " 'original': 'https://hhcdn.ru/employer-logo-original/62311.JPG'},\n", " 'vacancies_url': 'https://api.hh.ru/vacancies?employer_id=1175930',\n", " 'trusted': True},\n", " 'snippet': {'requirement': 'Образование повара.',\n", " 'responsibility': 'Приготовление домашней (кошерной) еды. Закупка продуктов. Поддержание чистоты на кухне.'},\n", " 'contacts': None,\n", " 'schedule': {'id': 'fullDay', 'name': 'Полный день'},\n", " 'working_days': [],\n", " 'working_time_intervals': [],\n", " 'working_time_modes': [],\n", " 'accept_temporary': False}],\n", " 'found': 3700,\n", " 'pages': 200,\n", " 'per_page': 10,\n", " 'page': 0,\n", " 'clusters': None,\n", " 'arguments': None,\n", " 'alternate_url': 'https://hh.ru/search/vacancy?area=1&enable_snippets=true&items_on_page=10&text=%D0%9F%D0%BE%D0%B2%D0%B0%D1%80'}" ] }, "execution_count": 1298, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": 1299, "metadata": {}, "outputs": [], "source": [ "# data scientist\n", "URL = 'https://api.hh.ru/vacancies'\n", "json_ds = []\n", "\n", "for page in range(0, 4):\n", " params = {\n", " 'text': \"Data Scientist\",\n", " 'area': 1,\n", " 'page': page,\n", " 'per_page': 100\n", " }\n", "\n", " req = requests.get(URL, params)\n", " data = json.loads(req.content.decode())\n", " json_ds += data['items']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Всппоминаем, какие столбцы нам нужны." ] }, { "cell_type": "code", "execution_count": 1300, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idnameemployer.nameaddress.metro.station_nameaddress.metro.line_nameaddress.metro.lataddress.metro.lngurl
044150000Стажер data scientistСИБУР, Группа компанийNaNNaNNaNNaNhttps://api.hh.ru/vacancies/44150000?host=hh.ru
144132320Аналитик Data ScientistСтоматоргNaNNaNNaNNaNhttps://api.hh.ru/vacancies/44132320?host=hh.ru
243896076Data Scientist в области компьютерного зренияFair Print & High StoreNaNNaNNaNNaNhttps://api.hh.ru/vacancies/43896076?host=hh.ru
343471916Data Scientist / Quantitative ResearcherЗнаменка КапиталКропоткинскаяСокольническая55.74529737.604217https://api.hh.ru/vacancies/43471916?host=hh.ru
443825932Junior Data ScientistИнститут прикладных экономических исследований...Юго-ЗападнаяСокольническая55.66314637.482852https://api.hh.ru/vacancies/43825932?host=hh.ru
...........................
32243582504IT RecruiterMcKinsey & CompanyБелорусскаяЗамоскворецкая55.77743937.582107https://api.hh.ru/vacancies/43582504?host=hh.ru
32343513052Аналитик данныхЛК Сименс ФинансNaNNaNNaNNaNhttps://api.hh.ru/vacancies/43513052?host=hh.ru
32441733915Client Consultant (market research, FMCG sector)NielsenIQТушинскаяТаганско-Краснопресненская55.82547937.437024https://api.hh.ru/vacancies/41733915?host=hh.ru
32542916086Product Manager (Analytics)SegmentoNaNNaNNaNNaNhttps://api.hh.ru/vacancies/42916086?host=hh.ru
32642779428Java Developer (Система управления знаниями и ...СбербанкКутузовскаяФилевская55.74054437.534100https://api.hh.ru/vacancies/42779428?host=hh.ru
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327 rows × 8 columns

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" ], "text/plain": [ " id name \\\n", "0 44150000 Стажер data scientist \n", "1 44132320 Аналитик Data Scientist \n", "2 43896076 Data Scientist в области компьютерного зрения \n", "3 43471916 Data Scientist / Quantitative Researcher \n", "4 43825932 Junior Data Scientist \n", ".. ... ... \n", "322 43582504 IT Recruiter \n", "323 43513052 Аналитик данных \n", "324 41733915 Client Consultant (market research, FMCG sector) \n", "325 42916086 Product Manager (Analytics) \n", "326 42779428 Java Developer (Система управления знаниями и ... \n", "\n", " employer.name \\\n", "0 СИБУР, Группа компаний \n", "1 Стоматорг \n", "2 Fair Print & High Store \n", "3 Знаменка Капитал \n", "4 Институт прикладных экономических исследований... \n", ".. ... \n", "322 McKinsey & Company \n", "323 ЛК Сименс Финанс \n", "324 NielsenIQ \n", "325 Segmento \n", "326 Сбербанк \n", "\n", " address.metro.station_name address.metro.line_name address.metro.lat \\\n", "0 NaN NaN NaN \n", "1 NaN NaN NaN \n", "2 NaN NaN NaN \n", "3 Кропоткинская Сокольническая 55.745297 \n", "4 Юго-Западная Сокольническая 55.663146 \n", ".. ... ... ... \n", "322 Белорусская Замоскворецкая 55.777439 \n", "323 NaN NaN NaN \n", "324 Тушинская Таганско-Краснопресненская 55.825479 \n", "325 NaN NaN NaN \n", "326 Кутузовская Филевская 55.740544 \n", "\n", " address.metro.lng url \n", "0 NaN https://api.hh.ru/vacancies/44150000?host=hh.ru \n", "1 NaN https://api.hh.ru/vacancies/44132320?host=hh.ru \n", "2 NaN https://api.hh.ru/vacancies/43896076?host=hh.ru \n", "3 37.604217 https://api.hh.ru/vacancies/43471916?host=hh.ru \n", "4 37.482852 https://api.hh.ru/vacancies/43825932?host=hh.ru \n", ".. ... ... \n", "322 37.582107 https://api.hh.ru/vacancies/43582504?host=hh.ru \n", "323 NaN https://api.hh.ru/vacancies/43513052?host=hh.ru \n", "324 37.437024 https://api.hh.ru/vacancies/41733915?host=hh.ru \n", "325 NaN https://api.hh.ru/vacancies/42916086?host=hh.ru \n", "326 37.534100 https://api.hh.ru/vacancies/42779428?host=hh.ru \n", "\n", "[327 rows x 8 columns]" ] }, "execution_count": 1300, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset_ds = pd.json_normalize(json_ds)[['id', 'name', 'employer.name', 'address.metro.station_name',\n", " 'address.metro.line_name', 'address.metro.lat', 'address.metro.lng', 'url']]\n", "dataset_ds" ] }, { "cell_type": "code", "execution_count": 1301, "metadata": {}, "outputs": [], "source": [ "# обработка массива скиллов\n", "def skills_parser(skills):\n", " res = []\n", " for l in range(len(skills)):\n", " res.append(skills[l]['name'])\n", " return res if res != [] else None" ] }, { "cell_type": "code", "execution_count": 1303, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "327 descriptions from 327 got\n" ] } ], "source": [ "# добавление описаний и требований\n", "descriptions_ds = []\n", "\n", "success = 0\n", "for cur_id in dataset_ds['id']:\n", " cur_url = f'https://api.hh.ru/vacancies/{cur_id}'\n", " cur_req = requests.get(cur_url)\n", " if cur_req.status_code == 200:\n", " success += 1\n", " else:\n", " print(\"error code\")\n", " \n", " cur_descr = json.loads(cur_req.content.decode())\n", " descriptions_ds += [{'id' : cur_descr['id'], \n", " 'skills' : skills_parser(cur_descr['key_skills']),\n", " 'description' : cur_descr['description'],\n", " 'experience' : cur_descr['experience'], \n", " 'salary' : cur_descr['salary']}]\n", " \n", "print(\"{} descriptions from {} got\".format(success, len(dataset_ds)))" ] }, { "cell_type": "code", "execution_count": 1304, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idskillsdescriptionsalaryexperience.idexperience.namesalary.fromsalary.tosalary.currencysalary.grossnameemployer.nameaddress.metro.station_nameaddress.metro.line_nameaddress.metro.lataddress.metro.lngurl
044150000[Python, SQL, R, machine learning]<p><strong>О НАС:</strong></p> <ul> <li>Одно и...NaNnoExperienceНет опытаNaNNaNNaNNaNСтажер data scientistСИБУР, Группа компанийNaNNaNNaNNaNhttps://api.hh.ru/vacancies/44150000?host=hh.ru
144132320[Python, MS Visio, Бизнес-анализ, Аналитически...<p><em><strong>Компания Стоматорг - первая час...NaNbetween1And3От 1 года до 3 лет180000.0250000.0RURTrueАналитик Data ScientistСтоматоргNaNNaNNaNNaNhttps://api.hh.ru/vacancies/44132320?host=hh.ru
243896076[C++, Тестирование, MATLAB, Гарант плюс, Бизне...<p>Стоимость работ: 150 000 рублей.</p> <p>Сро...NaNnoExperienceНет опыта150000.0150000.0RURFalseData Scientist в области компьютерного зренияFair Print & High StoreNaNNaNNaNNaNhttps://api.hh.ru/vacancies/43896076?host=hh.ru
343471916[Mathematical Statistics, Mathematical Program...<strong>Обязанности:</strong> <ul> <li>Разрабо...NaNbetween3And6От 3 до 6 лет250000.0NaNRURTrueData Scientist / Quantitative ResearcherЗнаменка КапиталКропоткинскаяСокольническая55.74529737.604217https://api.hh.ru/vacancies/43471916?host=hh.ru
443825932[PyTorch, TensorFlow, Scikit-learn, xgboost, P...<p><strong>Лаборатории анализа данных и отрасл...NaNbetween1And3От 1 года до 3 лет120000.0NaNRURTrueJunior Data ScientistИнститут прикладных экономических исследований...Юго-ЗападнаяСокольническая55.66314637.482852https://api.hh.ru/vacancies/43825932?host=hh.ru
......................................................
32243582504None<p><strong>Qualifications:</strong></p> <ul> <...NaNbetween3And6От 3 до 6 летNaNNaNNaNNaNIT RecruiterMcKinsey & CompanyБелорусскаяЗамоскворецкая55.77743937.582107https://api.hh.ru/vacancies/43582504?host=hh.ru
32343513052[SQL, Английский язык, Статистический анализ, ...<p>ООО ЛК «Сименс Финанс» (финансовое подразде...NaNbetween3And6От 3 до 6 летNaNNaNNaNNaNАналитик данныхЛК Сименс ФинансNaNNaNNaNNaNhttps://api.hh.ru/vacancies/43513052?host=hh.ru
32441733915[Market Research, Маркетинговые исследования, ...<p><strong>ABOUT THIS JOB</strong></p> <ul> <l...NaNbetween1And3От 1 года до 3 летNaNNaNNaNNaNClient Consultant (market research, FMCG sector)NielsenIQТушинскаяТаганско-Краснопресненская55.82547937.437024https://api.hh.ru/vacancies/41733915?host=hh.ru
32542916086[Python, Agile Project Management, Scrum, Goog...<p>Segmento – одна из крупнейших российских pr...NaNbetween3And6От 3 до 6 летNaNNaNNaNNaNProduct Manager (Analytics)SegmentoNaNNaNNaNNaNhttps://api.hh.ru/vacancies/42916086?host=hh.ru
32642779428[Git, Hibernate ORM, XML, Atlassian Jira, Java]<p>Ищем разработчика на стартап внутри большой...NaNbetween1And3От 1 года до 3 летNaNNaNNaNNaNJava Developer (Система управления знаниями и ...СбербанкКутузовскаяФилевская55.74054437.534100https://api.hh.ru/vacancies/42779428?host=hh.ru
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327 rows × 17 columns

\n", "
" ], "text/plain": [ " id skills \\\n", "0 44150000 [Python, SQL, R, machine learning] \n", "1 44132320 [Python, MS Visio, Бизнес-анализ, Аналитически... \n", "2 43896076 [C++, Тестирование, MATLAB, Гарант плюс, Бизне... \n", "3 43471916 [Mathematical Statistics, Mathematical Program... \n", "4 43825932 [PyTorch, TensorFlow, Scikit-learn, xgboost, P... \n", ".. ... ... \n", "322 43582504 None \n", "323 43513052 [SQL, Английский язык, Статистический анализ, ... \n", "324 41733915 [Market Research, Маркетинговые исследования, ... \n", "325 42916086 [Python, Agile Project Management, Scrum, Goog... \n", "326 42779428 [Git, Hibernate ORM, XML, Atlassian Jira, Java] \n", "\n", " description salary experience.id \\\n", "0

О НАС: